{"id":2298,"date":"2019-06-25T17:50:01","date_gmt":"2019-06-25T17:50:01","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2019\/06\/25\/want-to-learn-how-to-train-an-artificial-intelligence-model-ask-a-friend\/"},"modified":"2019-06-25T17:50:01","modified_gmt":"2019-06-25T17:50:01","slug":"want-to-learn-how-to-train-an-artificial-intelligence-model-ask-a-friend","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2019\/06\/25\/want-to-learn-how-to-train-an-artificial-intelligence-model-ask-a-friend\/","title":{"rendered":"Want to learn how to train an artificial intelligence model? Ask a friend."},"content":{"rendered":"

Author: Kim Martineau | MIT Quest for Intelligence<\/p>\n

\n

The\u00a0MIT Machine Intelligence Community<\/a> began with a few friends meeting over pizza to discuss landmark papers in machine learning. Three years later, the undergraduate club boasts 500 members, an active Slack channel, and an impressive lineup of student-led reading groups and workshops meant to demystify machine learning and artificial intelligence (AI) generally. This year, MIC and\u00a0MIT Quest for Intelligence<\/a>\u00a0joined forces to advance their common cause of making AI tools accessible to all.<\/p>\n

Starting last fall, the MIT Quest opened its offices to MIC members and extended access to IBM and Google-donated cloud credits, providing a boost of computing power to students previously limited to running their AI models on desktop machines loaded with extra graphics processors. The MIT Quest and MIC are now collaborating on a host of projects, independently and through MIT\u2019s\u00a0Undergraduate Research Opportunities Program<\/a> (UROP).<\/p>\n

\u201cWe heard about their mission to spread machine learning to all undergrads and thought, \u2018That\u2019s what we\u2019re trying to do \u2014 let\u2019s do it together!\u201d says\u00a0Joshua Joseph<\/a>, chief software engineer with the MIT Quest Bridge.\u00a0<\/p>\n

A makerspace for AI<\/strong><\/p>\n

U.S. Army ROTC students Ian Miller<\/a> and\u00a0Rishi Shah<\/a>\u00a0came to MIC for the free cloud credits, but stayed for the workshop on neural computing sticks. A compute stick allows mobile devices to do image processing on the fly, and when the cadets learned what one could do, they knew their idea for a portable computer vision system would work.\u00a0<\/p>\n

\u201cWithout that, we\u2019d have to send images to a central place to do all this computing,\u201d says Miller, a rising junior. \u201cIt would have been a logistical headache.\u201d<\/p>\n

Built in two months, for $200, their wallet-sized device is designed to plug into a tablet strapped to an Army soldier\u2019s chest and scan the surrounding area for cars and people. With more training, they say, it could learn to spot cellphones and guns. In May, the cadets demo’d their device at MIT\u2019s\u00a0Soldier Design Competition<\/a>\u00a0and were invited by an Army sergeant to visit Fort Devens to continue working on it.\u00a0<\/p>\n

<\/div>\n

Rose Wang<\/a>, a rising senior majoring in computer science, was also drawn to MIC by the free cloud credits, and a chance to work on projects with quest and other students. This spring, she used IBM cloud credits to run a reinforcement learning model that\u2019s part of her research with MIT Professor\u00a0Jonathan How<\/a>, training robot agents to cooperate on tasks that involve limited communication and information. She recently presented\u00a0her results<\/a>\u00a0at a workshop at the\u00a0International Conference on Machine Learning<\/a>.\u00a0\u00a0<\/p>\n

\u201cIt helped me try out different techniques without worrying about the compute bottleneck and running out of resources,\u201d she says.\u00a0<\/p>\n

Improving AI access at MIT<\/strong><\/p>\n

The MIC has launched several AI projects of its own. The most ambitious is Monkey, a container-based, cloud-native service that would allow MIT undergraduates to log in and train an AI model from anywhere, tracking the training as it progresses and managing the credits allotted to each student. On a Friday afternoon in April, the team gathered in a quest conference room as\u00a0Michael Silver<\/a>, a rising senior, sketched out the modules Monkey would need.\u00a0<\/p>\n

As Silver scrawled the words “Docker Image Build Service” on the board, the student assigned to research the module apologized. \u201cI didn\u2019t make much progress on it because I had three midterms!\u201d he said.\u00a0<\/p>\n

The planning continued, with\u00a0Steven Shriver<\/a>, a software engineer with the Quest Bridge, interjecting bits of advice. The students had assumed the container service they planned to use, Docker, would be secure. It isn\u2019t.\u00a0<\/p>\n

\u201cWell, I guess we have another task here,\u201d said Silver, adding the word \u201csecurity\u201d to the white board.\u00a0<\/p>\n

Later, the sketch would be turned into a design document and shared with the two\u00a0UROP students<\/a>\u00a0helping to execute Monkey.\u00a0The team hopes to launch sometime next year.\u00a0<\/p>\n

\u201cThe coding isn\u2019t the difficult part,\u201d says UROP student Amanda Li, a member of MIC Dev-Ops. \u201cIt\u2019s the exploring the server side of machine learning \u2014 Docker, Google Cloud, and the API. The most important thing I\u2019ve learned is how to efficiently design and pipeline a project as big as this.\u201d\u00a0<\/p>\n

Silver knew he wanted to be an AI engineer in 2016, when the computer program AlphaGo defeated the world\u2019s reigning Go champion. As a senior at Boston University Academy, Silver worked on natural language processing in the lab of MIT Professor\u00a0Boris Katz<\/a>, and has continued to work with Katz since coming to MIT. Seeking more coding experience, he left\u00a0HackMIT<\/a>, where he had been co-director, to join MIC Dev-Ops.<\/p>\n

\u201cA lot of students read about machine learning models, but have no idea how to train one,\u201d he says. \u201cEven if you know how to train one, you\u2019d need to save up a few thousand dollars to buy the GPUs to do it. MIC lets students interested in machine learning reach that next level.\u201d\u00a0<\/p>\n

Conceived by MIC members, a second project is focused on making AI research papers posted on\u00a0arXiv<\/a>\u00a0easier to explore. Nearly 14,000 academic papers are uploaded each month to the site, and although papers are tagged by field, drilling into subtopics can be overwhelming.<\/p>\n

Wang, for one, grew frustrated while doing a basic literature search on reinforcement learning. \u201cYou have a ton of data and no effective way of representing it to the user,\u201d she says. \u201cIt would have been useful to see the papers in a larger context, and to explore by number of citations or their relevance to each other.\u201d<\/p>\n

A third MIC project focuses on crawling MIT\u2019s hundreds of listservs for AI-related talks and events to populate a Google calendar. The tool will be closely patterned after an app Silver helped build during MIT\u2019s Independent Activities Period in January. Called Dormsp.am, the app classifies listserv emails sent to MIT undergraduates and plugs them into a calendar-email client. Students can then search for events by day or by a color-coded topic, such as tech, food, or jobs. Once Dormsp.am launches, Silver will adapt it to search for and post AI-related events at MIT to an MIC calendar.<\/p>\n

Silver says the team spent extra time on the user interface, taking a page from MIT Professor\u00a0Daniel Jackson<\/a>\u2019s\u00a0Software Studio<\/a> class. \u201cThis is an app that can live or die on its usability, so the front end is really important,\u201d he says.\u00a0\u00a0<\/p>\n

Wang is now collaborating with Moin Nadeem<\/a>, MIC\u2019s outgoing president, to build the visualization tool. It\u2019s exactly the kind of hands-on experience MIC was intended to provide, says Nadeem, a rising senior. \u201cStudents learn fundamental concepts in class but don\u2019t know how to implement them,\u201d he says. \u201cI\u2019m trying to build what freshman me would have liked to have had: a community of people excited to do interesting stuff with machine learning.\u201d\u00a0<\/p>\n<\/div>\n

Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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